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About: [FACTORIE](http://factorie.cs.umass.edu) is a toolkit for deployable probabilistic modeling, implemented as a software library in [Scala](http://scala-lang.org). It provides its users with a succinct language for creating [factor graphs](http://en.wikipedia.org/wiki/Factor_graph), estimating parameters and performing inference. It also has implementations of many machine learning tools and a full NLP pipeline. Changes:Initial Announcement on mloss.org.
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About: This provide a semi-supervised learning method based co-training for RGB-D object recognition. Besides, we evaluate four state-of-the-art feature learing method under the semi-supervised learning framework. Changes:Initial Announcement on mloss.org.
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About: Python module for machine learning multivariate time series Changes:Initial Announcement on mloss.org.
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About: An open-source framework for benchmarking of feature selection algorithms and cost functions. Changes:Initial Announcement on mloss.org.
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About: GPU-accelerated java deep neural networks Changes:Initial Announcement on mloss.org.
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About: Effect Displays for Linear, Generalized Linear, and Other Models Changes:Fetched by r-cran-robot on 2018-01-01 00:00:07.810965
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About: Block-Coordinate Frank-Wolfe Optimization for Structural SVMs Changes:Initial Announcement on mloss.org.
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About: This is an unoptimized implementation of the RFD binary descriptor, which is published in the following paper. B. Fan, et al. Receptive Fields Selection for Binary Feature Description. IEEE Transaction on Image Processing, 2014. doi: http://dx.doi.org/10.1109/TIP.2014.2317981 Changes:Initial Announcement on mloss.org.
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About: aiParts implements the High-Hope technique - options have models of emotions which affect and are affected by repeated attempts to solve a multi-decision problem. C++ classes for AI development. Changes:Initial Announcement on mloss.org.
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About: DRVQ is a C++ library implementation of dimensionality-recursive vector quantization, a fast vector quantization method in high-dimensional Euclidean spaces under arbitrary data distributions. It is an approximation of k-means that is practically constant in data size and applies to arbitrarily high dimensions but can only scale to a few thousands of centroids. As a by-product of training, a tree structure performs either exact or approximate quantization on trained centroids, the latter being not very precise but extremely fast. Changes:Initial Announcement on mloss.org.
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About: The fertilized forests project has the aim to provide an easy to use, easy to extend, yet fast library for decision forests. It summarizes the research in this field and provides a solid platform to extend it. Offering consistent interfaces to C++, Python and Matlab and being available for all major compilers gives the user high flexibility for using the library. Changes:Initial Announcement on mloss.org.
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About: Eblearn is an object-oriented C++ library that implements various Changes:Initial Announcement on mloss.org.
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About: Ohmm is a library for learning hidden Markov models by using Online EM algorithm. This library is specialized for large scale data; e.g. 1 million words. The output includes parameters, and estimation results. Changes:Initial Announcement on mloss.org.
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About: The open-source C-package fastICA implements the fastICA algorithm of Aapo Hyvarinen et al. (URL: http://www.cs.helsinki.fi/u/ahyvarin/) to perform Independent Component Analysis (ICA) and Projection Pursuit. fastICA is released under the GNU Public License (GPL). Changes:Initial Announcement on mloss.org.
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About: Jmlp is a java platform for both of the machine learning experiments and application. I have tested it on the window platform. But it should be applicable in the linux platform due to the cross-platform of Java language. It contains the classical classification algorithm (Discrete AdaBoost.MH, Real AdaBoost.MH, SVM, KNN, MCE,MLP,NB) and feature reduction(KPCA,PCA,Whiten) etc. Changes:Initial Announcement on mloss.org.
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About: Gaussian process RTS smoothing (forward-backward smoothing) based on moment matching. Changes:Initial Announcement on mloss.org.
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About: This is a set of MATLAB(R) functions and MEX files which I wrote to make working with this system somewhat bearable. They allow to call BLAS and LAPACK functions, which do very efficient dense [...] Changes:Initial Announcement on mloss.org.
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About: Efficient implementation of Semi-Stochastic Gradient Descent algorithm (S2GD) for training logistic regression (L2-regularized). Changes:Initial Announcement on mloss.org.
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About: Python toolbox for manifold optimization with support for automatic differentiation Changes:Initial Announcement on mloss.org.
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About: This library implements the Optimum-Path Forest classifier for unsupervised and supervised learning. Changes:Initial Announcement on mloss.org.
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